Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472360
Yi Zhang, Wee Peng Tay, K. H. Li, M. Esseghir, D. Gaïti
We formulate and study a multi-user multi-armed bandit (MAB) problem that exploits the temporal-spatial reuse of primary user (PU) channels so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve a NP-complete optimization problem at exponentially increasing time intervals. To avoid the high computation complexity at the central processor and the need for SU synchronization, we propose a heuristic distributed policy that incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for opportunistic spectrum access. Simulations suggest that our proposed policies significantly outperform the benchmark algorithms when spectrum temporal-spatial reuse is allowed.
{"title":"Opportunistic spectrum access with temporal-spatial reuse in cognitive radio networks","authors":"Yi Zhang, Wee Peng Tay, K. H. Li, M. Esseghir, D. Gaïti","doi":"10.1109/ICASSP.2016.7472360","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472360","url":null,"abstract":"We formulate and study a multi-user multi-armed bandit (MAB) problem that exploits the temporal-spatial reuse of primary user (PU) channels so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve a NP-complete optimization problem at exponentially increasing time intervals. To avoid the high computation complexity at the central processor and the need for SU synchronization, we propose a heuristic distributed policy that incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for opportunistic spectrum access. Simulations suggest that our proposed policies significantly outperform the benchmark algorithms when spectrum temporal-spatial reuse is allowed.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115275663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472587
Yi Jiang, Cong Shen, J. Dai
This paper presents a unified, optimization-driven solution for designing IIR and FIR notch filters with prescribed, possibly varying notch levels in the given stop-bands, and near unit magnitude frequency response at the pass-bands. Although the original IIR notch filter optimization problem is non-convex, we show that it can be well approximated by a convex problem, by replacing a non-positive semi-definite 2 × 2 Hermitian matrix with its nearest positive semi-definite counterpart. With this approach, the IIR filter design can be efficiently solved via Newton iteration. The same approach can be directly applied to the FIR filter design since it is a degenerated case of the IIR filter. Moreover, we show that the FIR design problem is convex and therefore can be solved optimally. Numerical examples are presented to verify the effectiveness of the proposed design.
{"title":"A unified approach to the design of IIR and FIR notch filters","authors":"Yi Jiang, Cong Shen, J. Dai","doi":"10.1109/ICASSP.2016.7472587","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472587","url":null,"abstract":"This paper presents a unified, optimization-driven solution for designing IIR and FIR notch filters with prescribed, possibly varying notch levels in the given stop-bands, and near unit magnitude frequency response at the pass-bands. Although the original IIR notch filter optimization problem is non-convex, we show that it can be well approximated by a convex problem, by replacing a non-positive semi-definite 2 × 2 Hermitian matrix with its nearest positive semi-definite counterpart. With this approach, the IIR filter design can be efficiently solved via Newton iteration. The same approach can be directly applied to the FIR filter design since it is a degenerated case of the IIR filter. Moreover, we show that the FIR design problem is convex and therefore can be solved optimally. Numerical examples are presented to verify the effectiveness of the proposed design.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115405850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7471687
Jacob Donley, C. Ritz, W. Kleijn
This paper proposes two methods for providing speech privacy between spatial zones in anechoic and reverberant environments. The methods are based on masking the content leaked between regions. The masking is optimised to maximise the speech intelligibility contrast (SIC) between the zones. The first method uses a uniform masker signal that is combined with desired multizone loudspeaker signals and requires acoustic contrast between zones. The second method computes a space-time domain masker signal in parallel with the loudspeaker signals so that the combination of the two emphasises the spectral masking in the targeted quiet zone. Simulations show that it is possible to achieve a significant SIC in anechoic environments whilst maintaining speech quality in the bright zone.
{"title":"Improving speech privacy in personal sound zones","authors":"Jacob Donley, C. Ritz, W. Kleijn","doi":"10.1109/ICASSP.2016.7471687","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471687","url":null,"abstract":"This paper proposes two methods for providing speech privacy between spatial zones in anechoic and reverberant environments. The methods are based on masking the content leaked between regions. The masking is optimised to maximise the speech intelligibility contrast (SIC) between the zones. The first method uses a uniform masker signal that is combined with desired multizone loudspeaker signals and requires acoustic contrast between zones. The second method computes a space-time domain masker signal in parallel with the loudspeaker signals so that the combination of the two emphasises the spectral masking in the targeted quiet zone. Simulations show that it is possible to achieve a significant SIC in anechoic environments whilst maintaining speech quality in the bright zone.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115599814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472744
Mitchell McLaren, L. Ferrer, A. Lawson
Using bottleneck features extracted from a deep neural network (DNN) trained to predict senone posteriors has resulted in new, state-of-the-art technology for language and speaker identification. For language identification, the features' dense phonetic information is believed to enable improved performance by better representing language-dependent phone distributions. For speaker recognition, the role of these features is less clear, given that a bottleneck layer near the DNN output layer is thought to contain limited speaker information. In this article, we analyze the role of bottleneck features in these identification tasks by varying the DNN layer from which they are extracted, under the hypothesis that speaker information is traded for dense phonetic information as the layer moves toward the DNN output layer. Experiments support this hypothesis under certain conditions, and highlight the benefit of using a bottleneck layer close to the DNN output layer when DNN training data is matched to the evaluation conditions, and a layer more central to the DNN otherwise.
{"title":"Exploring the role of phonetic bottleneck features for speaker and language recognition","authors":"Mitchell McLaren, L. Ferrer, A. Lawson","doi":"10.1109/ICASSP.2016.7472744","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472744","url":null,"abstract":"Using bottleneck features extracted from a deep neural network (DNN) trained to predict senone posteriors has resulted in new, state-of-the-art technology for language and speaker identification. For language identification, the features' dense phonetic information is believed to enable improved performance by better representing language-dependent phone distributions. For speaker recognition, the role of these features is less clear, given that a bottleneck layer near the DNN output layer is thought to contain limited speaker information. In this article, we analyze the role of bottleneck features in these identification tasks by varying the DNN layer from which they are extracted, under the hypothesis that speaker information is traded for dense phonetic information as the layer moves toward the DNN output layer. Experiments support this hypothesis under certain conditions, and highlight the benefit of using a bottleneck layer close to the DNN output layer when DNN training data is matched to the evaluation conditions, and a layer more central to the DNN otherwise.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115702089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472462
Jun Geng, L. Lai
In this paper, Bayesian quickest change-point detection problem with incomplete post-change information is considered. In particular, the observer knows that the post-change distribution belongs to a parametric distribution family, but he does not know the true value of the post-change parameter. Two problem formulations are considered in this paper. In the first formulation, we assume no additional prior information about the post-change parameter. In this case, the observer aims to design a detection algorithm to minimize the average (over the change-point) detection delay for all possible post-change parameters simultaneously subject to a worst case false alarm constraint. In the second formulation, we assume that there is a prior distribution on the possible value of the unknown parameter. For this case, we propose another formulation that minimizes the average (over both the change-point and the post-change parameter) detection delay subject to an average false alarm constraint. We propose a noval algorithm, which is termed as M-Shiryaev procedure, and show that the proposed algorithm is first order asymptotically optimal for both formulations considered in this paper.
{"title":"Bayesian quickest detection with unknown post-change parameter","authors":"Jun Geng, L. Lai","doi":"10.1109/ICASSP.2016.7472462","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472462","url":null,"abstract":"In this paper, Bayesian quickest change-point detection problem with incomplete post-change information is considered. In particular, the observer knows that the post-change distribution belongs to a parametric distribution family, but he does not know the true value of the post-change parameter. Two problem formulations are considered in this paper. In the first formulation, we assume no additional prior information about the post-change parameter. In this case, the observer aims to design a detection algorithm to minimize the average (over the change-point) detection delay for all possible post-change parameters simultaneously subject to a worst case false alarm constraint. In the second formulation, we assume that there is a prior distribution on the possible value of the unknown parameter. For this case, we propose another formulation that minimizes the average (over both the change-point and the post-change parameter) detection delay subject to an average false alarm constraint. We propose a noval algorithm, which is termed as M-Shiryaev procedure, and show that the proposed algorithm is first order asymptotically optimal for both formulations considered in this paper.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123094748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472847
Yue Zhang, Yuxiang Zhou, Jie Shen, Björn Schuller
In this work, we propose a novel approach for large-scale data enrichment, with the aim to address a major shortcoming of current research in computational paralinguistics, namely, looking at speaker attributes in isolation although strong interdependencies between them exist. The scarcity of multi-target databases, in which instances are labelled for different kinds of speaker characteristics, compounds this problem. The core idea of our work is to join existing data resources into one single holistic database with a multi-dimensional label space by using semi-supervised learning techniques to predict missing labels. In the proposed new Cross-Task Labelling (CTL) method, a model is first trained on the labelled training set of the selected databases for each individual task. Then, the trained classifiers are used for the crosslabelling of databases among each other. To exemplify the effectiveness of the `CTL' method, we evaluated it for likability, personality, and emotion recognition as representative tasks from the INTERSPEECH Computational Paralinguistics ChallengE (ComParE) series. The results show that `CTL' lays the foundation for holistic speech analysis by semi-autonomously annotating the existing databases, and expanding the multi-target label space at the same time, while achieving higher accuracy as the baseline performance of the challenges.
{"title":"Semi-autonomous data enrichment based on cross-task labelling of missing targets for holistic speech analysis","authors":"Yue Zhang, Yuxiang Zhou, Jie Shen, Björn Schuller","doi":"10.1109/ICASSP.2016.7472847","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472847","url":null,"abstract":"In this work, we propose a novel approach for large-scale data enrichment, with the aim to address a major shortcoming of current research in computational paralinguistics, namely, looking at speaker attributes in isolation although strong interdependencies between them exist. The scarcity of multi-target databases, in which instances are labelled for different kinds of speaker characteristics, compounds this problem. The core idea of our work is to join existing data resources into one single holistic database with a multi-dimensional label space by using semi-supervised learning techniques to predict missing labels. In the proposed new Cross-Task Labelling (CTL) method, a model is first trained on the labelled training set of the selected databases for each individual task. Then, the trained classifiers are used for the crosslabelling of databases among each other. To exemplify the effectiveness of the `CTL' method, we evaluated it for likability, personality, and emotion recognition as representative tasks from the INTERSPEECH Computational Paralinguistics ChallengE (ComParE) series. The results show that `CTL' lays the foundation for holistic speech analysis by semi-autonomously annotating the existing databases, and expanding the multi-target label space at the same time, while achieving higher accuracy as the baseline performance of the challenges.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114673256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472142
Umut Simsekli, R. Badeau, G. Richard, A. Cemgil
Model selection is a central topic in Bayesian machine learning, which requires the estimation of the marginal likelihood of the data under the models to be compared. During the last decade, conventional model selection methods have lost their charm as they have high computational requirements. In this study, we propose a computationally efficient model selection method by integrating ideas from Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) literature and statistical physics. As opposed to conventional methods, the proposed method has very low computational needs and can be implemented almost without modifying existing SG-MCMC code. We provide an upper-bound for the bias of the proposed method. Our experiments show that, our method is 40 times as fast as the baseline method on finding the optimal model order in a matrix factorization problem.
{"title":"Stochastic thermodynamic integration: Efficient Bayesian model selection via stochastic gradient MCMC","authors":"Umut Simsekli, R. Badeau, G. Richard, A. Cemgil","doi":"10.1109/ICASSP.2016.7472142","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472142","url":null,"abstract":"Model selection is a central topic in Bayesian machine learning, which requires the estimation of the marginal likelihood of the data under the models to be compared. During the last decade, conventional model selection methods have lost their charm as they have high computational requirements. In this study, we propose a computationally efficient model selection method by integrating ideas from Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) literature and statistical physics. As opposed to conventional methods, the proposed method has very low computational needs and can be implemented almost without modifying existing SG-MCMC code. We provide an upper-bound for the bias of the proposed method. Our experiments show that, our method is 40 times as fast as the baseline method on finding the optimal model order in a matrix factorization problem.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116977596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472876
V. Nguyen, K. Abed-Meraim, N. Linh-Trung
We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.
{"title":"Fast adaptive PARAFAC decomposition algorithm with linear complexity","authors":"V. Nguyen, K. Abed-Meraim, N. Linh-Trung","doi":"10.1109/ICASSP.2016.7472876","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472876","url":null,"abstract":"We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"720 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116980463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472670
Reza Lotfian, C. Busso
A speech emotion retrieval system aims to detect a subset of data with specific expressive content. Preference learning represents an appealing framework to rank speech samples in terms of continuous attributes such as arousal and valence. The training of ranking classifiers usually requires pairwise samples where one is preferred over the other according to a specific criterion. For emotional databases, these relative labels are not available and are very difficult to collect. As an alternative, they can be derived from existing absolute emotional labels. For continuous attributes, we can create relative rankings by forming pairs with high and low values of a specific attribute which are separated by a predefined margin. This approach raises questions about efficient approaches for building such a training set, which is important to improve the performance of the emotional retrieval system. This paper analyzes practical considerations in training ranking classifiers including optimum number of pairs used during training, and the margin used to define the relative labels. We compare the preference learning approach to binary classifier and regression models. The experimental results on a spontaneous emotional database indicate that a rank-based classifier with fine-tuned parameters outperforms the other two approaches in both arousal and valence dimensions.
{"title":"Practical considerations on the use of preference learning for ranking emotional speech","authors":"Reza Lotfian, C. Busso","doi":"10.1109/ICASSP.2016.7472670","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472670","url":null,"abstract":"A speech emotion retrieval system aims to detect a subset of data with specific expressive content. Preference learning represents an appealing framework to rank speech samples in terms of continuous attributes such as arousal and valence. The training of ranking classifiers usually requires pairwise samples where one is preferred over the other according to a specific criterion. For emotional databases, these relative labels are not available and are very difficult to collect. As an alternative, they can be derived from existing absolute emotional labels. For continuous attributes, we can create relative rankings by forming pairs with high and low values of a specific attribute which are separated by a predefined margin. This approach raises questions about efficient approaches for building such a training set, which is important to improve the performance of the emotional retrieval system. This paper analyzes practical considerations in training ranking classifiers including optimum number of pairs used during training, and the margin used to define the relative labels. We compare the preference learning approach to binary classifier and regression models. The experimental results on a spontaneous emotional database indicate that a rank-based classifier with fine-tuned parameters outperforms the other two approaches in both arousal and valence dimensions.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117129408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472234
Hong Jiang, Yiwei Lu, Shunyou Yao
Traditional methods of target parameter estimation in MIMO radar are carried out under the assumption that the number of observations is much larger than the number of array elements. However, their estimation performance will decline for the MIMO radar with large arrays and insufficient observations. In this paper, we investigate the situation in bistatic MIMO radar that the product of the numbers of the transmit and receive elements and the number of observations grow at the same rate. We propose a robust method for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in non-Gaussian noise environment. The method uses robust M-estimator to form an estimate of the covariance matrix, and then applies random matrix theory (RMT) and polynomial rooting algorithm to receive accurate DOD and DOA estimates for large scale MIMO radar. The simulation results demonstrate the robustness and improvement in accuracy.
{"title":"Random matrix based method for joint DOD and DOA estimation for large scale MIMO radar in non-Gaussian noise","authors":"Hong Jiang, Yiwei Lu, Shunyou Yao","doi":"10.1109/ICASSP.2016.7472234","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472234","url":null,"abstract":"Traditional methods of target parameter estimation in MIMO radar are carried out under the assumption that the number of observations is much larger than the number of array elements. However, their estimation performance will decline for the MIMO radar with large arrays and insufficient observations. In this paper, we investigate the situation in bistatic MIMO radar that the product of the numbers of the transmit and receive elements and the number of observations grow at the same rate. We propose a robust method for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in non-Gaussian noise environment. The method uses robust M-estimator to form an estimate of the covariance matrix, and then applies random matrix theory (RMT) and polynomial rooting algorithm to receive accurate DOD and DOA estimates for large scale MIMO radar. The simulation results demonstrate the robustness and improvement in accuracy.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117261281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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